Statistics of sea-effect snowfall along the Finnish coastline based on regional climate model data
Bibliographic record
Abstract
Abstract. The formation of convective sea-effect snowfall (i.e., snow bands) is triggered by cold air outbreaks over a relatively warm and open sea. Snow bands can produce intense snowfall which can last for several days over the sea and potentially move towards the coast depending on wind direction. We defined the meteorological conditions which statistically favor the formation of snow bands over the north-eastern Baltic Sea of the Finnish coastline and investigated the spatio-temporal characteristics of these snow bands. A set of criteria, which have been previously shown to be able to detect the days favoring sea-effect snowfall for Swedish coastal area, were refined for Finland based on four case study simulations, utilizing a convection-permitting numerical weather prediction (NWP) model (HARMONIE-AROME). The main modification of the detection criteria concerned the threshold for 10 m wind speed: the generally assumed threshold value of 10 m s−1 was decreased to 7 m s−1. The refined criteria were then applied to regional climate model (RCA4) data, for an 11-year time period (2000–2010). When only considering cases in Finland with onshore wind direction, we found on average 3 d yr−1 with favorable conditions for coastal sea-effect snowfall. The heaviest convective snowfall events were detected most frequently over the southern coastline. Statistics of the favorable days indicated that the lower 10 m wind speed threshold improved the representation of the frequency of snow bands. For most of the favorable snow band days, the location and order of magnitude of precipitation were closely captured, when compared to gridded observational data for land areas and weather radar reflectivity images. Lightning were observed during one third of the favorable days over the Baltic Sea area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".